R K Brojen Singh | Searcharxiv

arXiv2026

Quantifying biofilm-virulence index to predict antifungal resistance in Candida albicans

Candida albicans is a commensal microorganism that causes opportunistic infections, such as oral candidiasis, vaginitis affecting females, newborns, and immunocompromised patients. Biofilm formation can lead to a commensal organism becoming a life-threatening organism by introducing antifungal resistance. The experiment we did combines crystal violet staining for biofilm biomass and CFU counts to statistically construct an additive BVI model by analysing the experimental data. Our study on the data proposes a Biofilm-Virulence Index (BVI) as a novel and quantitative parameter for assessing antifungal drug resistance in Candida albicans. The effect of the drugs on inhibition zone diameter is twofold, first, linear increase with time during early biofilm formation, second, stabilizing in later phases and correlating directly with virulence. Most BVI values remained in the mild infection range, indicating successful virulence reduction by antifungal drugs. The BVI model model combines the study of biofilm and viable cell count in a single parameter. So, this makes comparison between samples easier during biofilm analysis. Findings suggest that combination of CFU and biofilm measurement may improve interpretation of antifungal response in Candida albicans. This approach could be useful in future experimental studies investigating biofilm associated resistance.

q-bio.OT↗

arXiv2025

Optimal Control of Lantana camara: An Entropy-Based Sustainable Strategy

Framing control policies to mitigate the impact of invasive plants on indigenous biodiversity within the Sustainable Development Goals (SDG) framework is the primary objective of this work. Using reported ecological dynamics of the invasive species \textit{Lantana camara}, we develop a minimal three-species network model, where each node follows generalized Lotka-Volterra (GLV) dynamical equations. Employing Lie algebra and network control theory, we establish the model's controllability and accessibility criteria. Through nonlinear optimization programming, we derive sustainable policies for controlling abundances of \textit{Lantana camara}. We also have used Shannon entropy as an indicator to assess the sustainability of these optimal policies. The analysis of the sensitivity measured using this technique reveals that the control strategy is critically dependent on the ratio of the intrinsic growth rates of the \textit{Lantana camara} and the control plant. Thus, we get a modular algorithmic decision support mechanism for designing control policies to manage \textit{Lantana camara} abundances.

q-bio.PE↗